Simulation and modelling tools for quantitative safety assessments of unmanned aircraft systems and operations

This paper presents two simple simulation and modelling tools designed to aid in the safety assessment required for unmanned aircraft operations within unsegregated airspace. First, a fast pair-wise encounter generator is derived to simulate the See and Avoid environment. The utility of the encounter generator is demonstrated through the development of a hybrid database and a statistical performance evaluation of an autonomous See and Avoid decision and control strategy. Second, an unmanned aircraft mission generator is derived to help visualise the impact of multiple persistent unmanned operations on existing air traffic. The utility of the mission generator is demonstrated through an example analysis of a mixed airspace environment using real traffic data in Australia. These simulation and modelling approaches constitute a useful and extensible set of analysis tools, that can be leveraged to help explore some of the more fundamental and challenging problems facing civilian unmanned aircraft system integration.

How Can Sustainable Agricultural Systems Promote Food Security in a Changing Climate?

A theoretical framework of the link between climate change, rural development, sustainable agriculture, poverty, and food security is presented. Some options to respond to climate change are described. Current knowledge and potential effects on agricultural productivity is discussed. Necessary conditions for successful adaptation includes secured property rights to land, institutions that make market access possible and credit possibilities. The options of mitigation and enhanced adaptive capacity and the requirements for their implementation are discussed.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

Representation of thermodynamic properties of ternary systems and its application to the system silver-gold-copper at 1350 K

The article presents a generalized analytical expression for description of the integral excess Gibbs free energy of mixing of a ternary system. Twelve constants of the equation are assessed by the least mean squares regressional analysis of the experimental integral excess data of the constituent binaries; three ternary parameters are evaluated by a regressional analysis based on the partial experimental data of a component of the ternary system. The assessed values of the ternary parameters describe the nature of the ternary interaction in the system. Activities and isoactivities of the components in the Ag-Au-Cu system at 1350 K are calculated and found to be in good agreement with the experimental data. This analytical treatment is particularly useful to ternary systems where the thermodynamic data are available from different sources.

Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in(2) using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in(2) with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give similar to 10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.

Fisheries marketing systems and consumer preferences in Puttalam District Sri-Lanka

The aim of this study was to understand the current and historic market situation for inland fish and it’s substitutes in order to identify which of the various production opportunities presented by the seasonal tank resource might have greatest relevance for marginal communities in the Dry-zone. Regional and sub-regional market networks for fish and meat products were investigated, ranking and scoring exercises used to characterise consumer demand in rain-fed areas of North West Province and secondary data sources were used to assess historic patterns of demand and supply [PDF contains 57 pages]

Aquaculture and food security, poverty alleviation and nutrition in Ghana: Case study prepared for the Aquaculture for Food Security, Poverty Alleviation and Nutrition project

This study provides an overview of the aquaculture sector in Ghana. It assesses the actual and potential contribution of aquaculture to poverty reduction and food security, and identifies enabling conditions for and drivers of the development of Ghana’s aquaculture sector. The study uses data collected from a variety of primary and secondary sources, including key informant interviews with actors within the aquaculture sector and relevant secondary literature.

Futures of inland aquatic agricultural systems and implications for fish agri-food systems in southern Africa

The CGIAR Research Program on Aquatic Agricultural Systems (AAS) is collaborating with partners to develop and implement a foresight-based engagement with diverse stakeholders linked to aquatic agricultural systems. The program’s aim is to understand the implications of current drivers of change for fish agri-food systems, and consequently food and nutrition security, in Africa, Asia and the Pacific. Partners include the Global Forum on Agricultural Research (GFAR), the Forum for Agricultural Research in Africa (FARA) and the African Union’s New Partnership for Africa’s Development (AU-NEPAD). A key part of the program was a participatory scenario-building workshop held in July 2015 under the theme of "futures of aquatic agricultural systems and implications for fish agri-food systems in southern Africa." The objectives for the workshop were (i) to engage local stakeholders in exploring plausible futures of aquatic agricultural systems, and (ii) to broker and catalyze collaborative plans of action based on the foresight analysis. This report presents technical findings from the workshop. The CGIAR Research Program on Aquatic Agricultural Systems (AAS) is collaborating with partners to develop and implement a foresight-based engagement with diverse stakeholders linked to aquatic agricultural systems. The program’s aim is to understand the implications of current drivers of change for fish agri-food systems, and consequently food and nutrition security, in Africa, Asia and the Pacific. Partners include the Global Forum on Agricultural Research (GFAR), the Forum for Agricultural Research in Africa (FARA) and the African Union’s New Partnership for Africa’s Development (AU-NEPAD). A key part of the program was a participatory scenario-building workshop held in July 2015 under the theme of "futures of aquatic agricultural systems and implications for fish agri-food systems in southern Africa." The objectives for the workshop were (i) to engage local stakeholders in exploring plausible futures of aquatic agricultural systems, and (ii) to broker and catalyze collaborative plans of action based on the foresight analysis. This report presents technical findings from the workshop.

The potential of community fish refuges (CFRs) in rice field agro-ecosystems for improving food and nutrition security in the Tonle Sap region

The fisheries sector in Cambodia contributes 8%–12% to national GDP and 25% - 30% to agricultural GDP, with an estimated 4.5 million people involved in fishing and associated trades. Fish and other aquatic animals are important food sources, contributing an estimated national average of 60% - 70% of total animal protein intake. Of the 2013 total fish production, 550,000 metric tons were harvested from freshwater habitats, of which rice field fisheries and small-scale family fisheries contributed approximately 20%. The productivity and value of rice field fisheries to households in rural Cambodia has been highlighted in a number of previous studies. The Fisheries Administration of the Ministry of Agriculture, Forestry and Fisheries plans to increase productivity from rice field fisheries and aquaculture at an annual rate of 15% to maintain supply for a growing population. This report draws mainly on the baseline and monitoring data from the Rice Field Fisheries Enhancement Project (RFFEP) during its implementation between 2012 and 2014. Reference is also made to the Fish on Farms project to highlight the relative contribution of fish from small-scale aquaculture compared to wild-caught fish.

Coexistence of high-bit-rate quantum key distribution and data on optical fiber

Quantum key distribution (QKD) uniquely allows distribution of cryptographic keys with security verified by quantum mechanical limits. Both protocol execution and subsequent applications require the assistance of classical data communication channels. While using separate fibers is one option, it is economically more viable if data and quantum signals are simultaneously transmitted through a single fiber. However, noise-photon contamination arising from the intense data signal has severely restricted both the QKD distances and secure key rates. Here, we exploit a novel temporal-filtering effect for noise-photon rejection. This allows high-bit-rate QKD over fibers up to 90 km in length and populated with error-free bidirectional Gb/s data communications. With high-bit rate and range sufficient for important information infrastructures, such as smart cities and 10 Gbit Ethernet, QKD is a significant step closer towards wide-scale deployment in fiber networks.

Phase Diagram of [Amim]Cl plus Salt Aqueous Biphasic Systems and Its Application for [Amim]Cl Recovery

In this study, binodal curves and tie line data of [Amim]Cl + salt (K3PO4, K2HPO4, K2CO3) + water aqueous biphasic systems (ABS) were measured and correlated satisfactorily with the Merchuk equation and Othmer-Tobias and Bancroft equations, respectively. [Amim]Cl could be recovered from aqueous solutions using the ABS, and the recovery efficiency could reach 96.80%. The recovery efficiency was influenced by the concentrations of the salts and their Homeister series: K3PO4 > K2HPO4 > K2CO3. Our method provides a new and effective route for the recovery of hydrophilic IL using [Amim]Cl + salt + water ABS from aqueous solutions.

Wireless and Physical Security via Embedded Sensor Networks

Wireless Intrusion Detection Systems (WIDS) monitor 802.11 wireless frames (Layer-2) in an attempt to detect misuse. What distinguishes a WIDS from a traditional Network IDS is the ability to utilize the broadcast nature of the medium to reconstruct the physical location of the offending party, as opposed to its possibly spoofed (MAC addresses) identity in cyber space. Traditional Wireless Network Security Systems are still heavily anchored in the digital plane of "cyber space" and hence cannot be used reliably or effectively to derive the physical identity of an intruder in order to prevent further malicious wireless broadcasts, for example by escorting an intruder off the premises based on physical evidence. In this paper, we argue that Embedded Sensor Networks could be used effectively to bridge the gap between digital and physical security planes, and thus could be leveraged to provide reciprocal benefit to surveillance and security tasks on both planes. Toward that end, we present our recent experience integrating wireless networking security services into the SNBENCH (Sensor Network workBench). The SNBENCH provides an extensible framework that enables the rapid development and automated deployment of Sensor Network applications on a shared, embedded sensing and actuation infrastructure. The SNBENCH's extensible architecture allows an engineer to quickly integrate new sensing and response capabilities into the SNBENCH framework, while high-level languages and compilers allow novice SN programmers to compose SN service logic, unaware of the lower-level implementation details of tools on which their services rely. In this paper we convey the simplicity of the service composition through concrete examples that illustrate the power and potential of Wireless Security Services that span both the physical and digital plane.

On efficient data integrity and data origin authentication for wireless sensor networks utilising block cipher design techniques

Analysis of the generic attacks and countermeasures for block cipher based message authentication code algorithms (MAC) in sensor applications is undertaken; the conclusions are used in the design of two new MAC constructs Quicker Block Chaining MAC1 (QBC-MAC1) and Quicker Block Chaining MAC2 (QBC-MAC2). Using software simulation we show that our new constructs point to improvements in usage of CPU instruction clock cycle and energy requirement when benchmarked against the de facto Cipher Block Chaining MAC (CBC-MAC) based construct used in the TinySec security protocol for wireless sensor networks.